In this work, we have class imbalance during training.
The dataset imbalance causes (i) inefficient training, because it has few samples (of some kinds) in the training stage, the network may not observe all the samples; and, by having a small number of samples, (ii) the network can fall into overfitting and degenerate the model.
To address this problem, we use median frequency (counting) balancing [1]}, defined by
\( \tau _c = \frac{\bar{f}}{f(c)},\) 
